Here we present graphs of the results of the Toxosources consumer survey. Raw data files are available at…
library( RColorBrewer )
library( tidyverse )
library( haven )
library( here )
setwd(here("consumption", "GFK survey"))
source( "functions.R" )
df_survey_info <- read_survey_info()
df_survey_freq <- read_survey_freq()
df_survey_process <- read_survey_process()
df_survey_type <- read_survey_type()
my_theme <- theme( panel.background = element_blank(),
panel.grid.major = element_blank(),
strip.text = element_text( size=15 ),
axis.title = element_text( size=15 ),
axis.text = element_text( size=15 ),
plot.title = element_text( size=25 ),
legend.title = element_text( size=15 ),
legend.text = element_text( size=15 ),
plot.margin = margin(0, 1, 0, 0, "cm"),
legend.position="bottom" )
This category includes all meat, including meat which is heated thoroughly and therefore microbiologically safe.
df_survey_process %>%
dplyr::select( freq_meat_general, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_general ) %>%
plot_freq( "Consumption frequency of meat", freq_meat_general )
#ggsave("figures/freq_meat_general.png", width = 35, height = 20, units="cm", dpi=300 )
freq_meat_labels = c(
"freq_meat_beef_veal"="Veal",
"freq_meat_pork"="Pork",
"freq_meat_poultry"="Poultry",
"freq_meat_lamb"="Lamb",
"freq_meat_mutton"="Mutton",
"freq_meat_horse"="Horse",
"freq_meat_other"="Other",
"freq_meat_game"="Game",
"freq_meat_wild_birds"="Birds",
"freq_meat_mince_beef"="Beef",
"freq_meat_mince_pork"="Pork",
"freq_meat_mince_mix"="Mix",
"freq_meat_mince_misc"="Misc",
"freq_meat_sausage"="Sausage",
"freq_meat_spread"="Spread",
"freq_meat_cured_pork"="Cured Pork",
"freq_meat_cured_beef"="Cured Beef",
"freq_meat_cured_misc"="Cured Misc",
"freq_meat_dry_fermented"="Dry Fermented",
"freq_meat_dried"= "Dried Meat",
"freq_meat_carpaccio"="Carpaccio",
"freq_meat_tartare"= "Tartare" )
df_survey_freq %>%
dplyr::select( starts_with("freq_meat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_meat"), names_to = "meat", values_to = "consumed" ) %>%
calc_perc( meat, consumed ) %>%
plot_freq( "Frequency of consumption of meat products",
fill_by=consumed, wrap_by=meat, my_labels=freq_meat_labels )+
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/freq_meat.png", width = 35, height = 25, units="cm", dpi=300)
freq_labels_raw= c( "freq_raw_shell" = "Shellfish",
"freq_raw_meatball" = "Meatball",
"freq_raw_milk" = "Raw Milk" )
df_survey_freq %>% #
dplyr::select( starts_with("freq_raw"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_raw"), names_to = "raw", values_to = "consumed" ) %>%
calc_perc( raw, consumed ) %>%
plot_freq( "Frequency of consumption of raw products",
fill_by=consumed, wrap_by=raw, my_labels=freq_labels_raw )+
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/freq_raw.png", width = 35, height = 25, units="cm", dpi=300)
freq_veg_labels = c("freq_veg_berries" = "Berries",
"freq_veg_pome_fr" = "Pomme Fruit",
"freq_veg_brassic" = "Brassica",
"freq_veg_cucurbi" = "Cucurbi",
"freq_veg_tomato" = "Tomato",
"freq_veg_peppers" = "Peppers",
"freq_veg_fungi" = "Fungi",
"freq_veg_herbs" = "Herbs",
"freq_veg_leafy_veg" = "Leafy Vegetables",
"freq_veg_sprouts" = "Sprouts",
"freq_veg_leafy_misc" = "Leafy Misc",
"freq_veg_roots" = "Roots",
"freq_veg_stems" = "Stems",
"freq_veg_fermented" = "Fermented",
"freq_veg_asparagus" = "Asperagus",
"freq_veg_sugarsnaps" = "Sugarsnaps",
"freq_veg_ginger" = "Ginger",
"freq_veg_salicornia" = "Salicornia" )
df_survey_freq %>%
dplyr::select( starts_with("freq_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "freq_veg"), names_to = "veg", values_to = "consumed" ) %>%
calc_perc( veg, consumed ) %>%
plot_freq( "Frequency of consumption of vegetables",
fill_by=consumed, wrap_by=veg, my_labels=freq_veg_labels )
#ggsave("figures/freq_veg.png", width = 15, height = 15)
cz_labels= c( "freq_cz_tatarak_divocaka" = "Tatarak divocaka",
"freq_cz_pecene_veprove" = "Pecene veprove",
"freq_cz_veprovy_mozecek" = "Veprovy mozecek",
"freq_cz_jitrnice" = "Jitrnice",
"freq_cz_jelito" = "Jelito",
"freq_cz_tlacenka" = "Tlacenka" )
df_survey_freq %>%
dplyr::select( starts_with("freq_cz"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Czech Republic" ) %>%
pivot_longer( cols=starts_with( "freq_cz"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Czech Republic specialty products",
fill_by=consumed, my_labels=cz_labels )
#ggsave("figures/freq_cz_specialty.png", width = 35, height = 25, units="cm", dpi=300)
de_labels= c( "freq_de_hackepeter" = "hackepeter",
"freq_de_knackwurst" = "knackwurst" )
df_survey_freq %>%
dplyr::select( starts_with("freq_de"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Germany" ) %>%
pivot_longer( cols=starts_with( "freq_de"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of German specialty products",
fill_by=consumed, my_labels=de_labels )
#ggsave("figures/freq_de_specialty.png", width = 35, height = 25, units="cm", dpi=300)
dk_labels <- c( "freq_dk_medisterpolse" = "Medisterpolse",
"freq_dk_rullepolse" = "Rullepolse",
"freq_dk_hjerter_flodesovs" = "Hjerter flodesovs" )
df_survey_freq %>%
dplyr::select( starts_with("freq_dk"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Denmark" ) %>%
pivot_longer( cols=starts_with( "freq_dk"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Danish specialty products",
fill_by=consumed, my_labels=dk_labels )
#ggsave("figures/freq_dk_specialty.png", width = 35, height = 25, units="cm", dpi=300)
fr_labels <- c( "freq_fr_figatelle" = "Figatelle",
"freq_fr_mettwurst" = "Mettwurst",
"freq_fr_museau_porc" = "Museau Porc",
"freq_fr_museau_boeuf" = "Museau Boeuf" )
df_survey_freq %>%
dplyr::select( starts_with("freq_fr"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "France" ) %>%
pivot_longer( cols=starts_with( "freq_fr"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of French specialty products",
fill_by=consumed, my_labels=fr_labels )
#ggsave("figures/freq_fr_specialty.png", width = 35, height = 25, units="cm", dpi=300)
nl_labels <- c( "freq_nl_filet_americain" = "Filet Americain",
"freq_nl_paardenrookvlees" = "Paardenrookvlees",
"freq_nl_ossenworst" = "Ossenworst",
"freq_nl_theeworst" = "Theeworst",
"freq_nl_rosbief" = "Rosbief" )
df_survey_freq %>%
dplyr::select( starts_with("freq_nl"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "The Netherlands" ) %>%
pivot_longer( cols=starts_with( "freq_nl"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Dutch specialty products",
fill_by=consumed, my_labels=nl_labels )
#ggsave("figures/freq_nl_specialty.png", width = 35, height = 25, units="cm", dpi=300)
no_labels <- c( "freq_no_lammerull" = "Lammerull",
"freq_no_sylterull" = "Sylterull",
"freq_no_kjottrull" = "Kjottrull",
"freq_no_smalahove" = "Smalahove",
"freq_no_whale" = "Whale" )
df_survey_freq %>%
dplyr::select( starts_with("freq_no"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Norway" ) %>%
pivot_longer( cols=starts_with( "freq_no"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Norwegian specialty products",
fill_by=consumed, my_labels=no_labels )
#ggsave("figures/freq_no_specialty.png", width = 35, height = 25, units="cm", dpi=300)
es_labels <- c( "freq_es_morcilla" = "Morcilla",
"freq_es_lomo_orza" = "Lomo Orza" )
df_survey_freq %>%
dplyr::select( starts_with("freq_es"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter( country == "Spain" ) %>%
pivot_longer( cols=starts_with( "freq_es"), names_to = "specialty", values_to = "consumed" ) %>%
calc_perc( specialty, consumed ) %>%
plot_freq_specialty( "Frequency of consumption of Spanish specialty products",
fill_by=consumed, my_labels=es_labels )
#ggsave("figures/freq_es_specialty.png", width = 35, height = 25, units="cm", dpi=300)
labels_poultry = c( "type_poultry_chicken" = "Chicken",
"type_poultry_turkey" = "Turkey",
"type_poultry_duck" = "Duck",
"type_poultry_goose" = "Goose",
"type_poultry_ostrich" = "Ostrich",
"type_poultry_misc" = "Misc" )
df_survey_type %>%
dplyr::select( starts_with("type_poultry"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "poultry", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( poultry, consumed ) %>%
plot_freq( "Type of poultry consumed", fill_by=consumed,
wrap_by=poultry, my_labels=labels_poultry )
#ggsave("figures/type_poultry.png", width=20, height=20, units="cm" )
labels_goat <- c(
"type_sheep_lamb" = "Sheep lamb",
"type_sheep_adult" = "Sheep adult",
"type_goat_lamb" = "Goat lamb",
"type_goat_adult" = "Goat adult" )
df_survey_type %>%
dplyr::select( starts_with("type_sheep"), starts_with("type_goat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( livestock, consumed ) %>%
plot_freq( "Type of goat or sheep consumed", fill_by=consumed,
wrap_by=livestock, my_labels=labels_goat )
#ggsave("figures/type_sheep_goat.png", width=20, height=20, units="cm" )
labels_livestock <- c(
"type_livestock_donkey" = "Donkey",
"type_livestock_buffalo" = "Buffalo",
"type_livestock_rabbit" = "Rabbit",
"type_livestock_reindeer" = "Reindeer",
"type_livestock_misc" = "Misc" )
df_survey_type %>%
dplyr::select( starts_with("type_livestock"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "livestock", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( livestock, consumed ) %>%
plot_freq( "Type of livestock consumed", fill_by=consumed,
wrap_by=livestock, my_labels=labels_livestock )
#ggsave("figures/type_livestock.png", width=30, height=20, units="cm" )
labels_game <- c(
"type_game_boar" = "Boar",
"type_game_rabbit" = "Rabbit",
"type_game_hare" = "Hare",
"type_game_reindeer" = "Reindeer",
"type_game_moose" = "Moose",
"type_game_misc_deer" = "Misc. Deer",
"type_game_mouflon" = "Mouflon",
"type_game_chamois" = "Chamois",
"type_game_ibex" = "Ibex",
"type_game_misc" = "Misc." )
df_survey_type %>%
dplyr::select( starts_with("type_game"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "game", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( game, consumed ) %>%
plot_freq( "Type of game consumed", fill_by=consumed,
wrap_by=game, my_labels=labels_game )
#ggsave("figures/type_game.png", width=30, height=20, units="cm" )
labels_wildbird <- c(
"type_wild_bird_duck" = "Duck",
"type_wild_bird_goose" = "Goose",
"type_wild_bird_pheasant" = "Pheasant",
"type_wild_bird_quail" = "Quail",
"type_wild_bird_partridge" = "Partridge",
"type_wild_bird_grouse" = "Grouse",
"type_wild_bird_guineafowl" = "Guineafowl",
"type_wild_bird_pigeon" = "Pigeon",
"type_wild_bird_ptarmigan" = "Ptarmigan",
"type_wild_bird_misc" = "Misc." )
df_survey_type %>%
dplyr::select( starts_with("type_wild"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "type_"), names_to = "wildbird", values_to = "consumed" ) %>%
mutate( consumed = ifelse( consumed, "Consumed", "Not consumed") %>% as.factor() ) %>%
mutate( consumed = fct_relevel( consumed, "Consumed", "Not consumed")) %>%
calc_perc( wildbird, consumed ) %>%
plot_freq( "Type of wild bird consumed", fill_by=consumed,
wrap_by=wildbird, my_labels=labels_wildbird )
#ggsave("figures/type_wild_birds.png", width=20, height=20, units="cm" )
df_survey_process %>%
dplyr::select( freq_veg_sold_ready, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_veg_sold_ready ) %>%
plot_freq( "Frequency of consuming vegetable and fruit RTE", freq_veg_sold_ready )
#ggsave("figures/freq_veg_ready.png", width=30, height=15, units="cm", dpi=300 )
df_survey_process %>%
dplyr::select( freq_veg_sold_small_scale, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_veg_sold_small_scale ) %>%
plot_freq("Frequency of vegetables bought from small scale producer", freq_veg_sold_small_scale )
#ggsave("figures/freq_veg_small.png", width=30, height=20, units="cm")
df_survey_process %>%
dplyr::select( freq_meat_organic, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_organic ) %>%
plot_freq( "Frequency of buying organic meat", freq_meat_organic )
#ggsave("figures/freq_meat_organic.png", width=35, height = 15, units="cm")
df_survey_process %>%
dplyr::select( freq_meat_frozen, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( freq_meat_frozen ) %>%
plot_freq( "Frequency of buying meat frozen", freq_meat_frozen )
#ggsave("figures/freq_meat_frozen.png", width=20, height = 15, units="cm")
store_labels <- c(
"store_veg_berries" = "Berries",
"store_veg_herbs" = "Herbs",
"store_veg_leaf" = "Leafy Greens",
"store_veg_fermented" = "Fermented vegetables" )
df_survey_process %>%
dplyr::select( starts_with("store_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>%
calc_perc( prep, stored ) %>%
plot_freq( "Frequency of storing vegetable in fridge or freezer",
fill_by=stored, wrap_by=prep, my_labels=store_labels )
#ggsave("figures/store_veg.png", width = 30, height = 20, units="cm" )
store_meat_labels <- c(
"store_meat_cut" = "Meat Cuts",
"store_meat_mince" = "Minced Meat",
"store_meat_sausage_fresh" = "Fresh Sausage",
"store_meat_spread" = "Meat Spread",
"store_meat_cured" = "Cured Meat",
"store_meat_dry_fermented" = "Dry Fermented Sausage",
"store_meat_dry" = "Dry Sausage"
)
df_survey_process %>%
dplyr::select( starts_with("store_meat"), id ) %>%
left_join( df_survey_info, by="id") %>%
pivot_longer( cols=starts_with( "store_"), names_to = "prep", values_to = "stored" ) %>%
calc_perc( prep, stored ) %>%
plot_freq( my_title="Frequency of storing meat in fridge or freezer",
fill_by=stored, wrap_by=prep, my_labels=store_meat_labels ) +
guides(fill = guide_legend(nrow = 3, byrow = T))
#ggsave("figures/store_meat.png", height = 25, width = 37, units="cm", dpi=300 )
df_survey_process %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( quality_storage_meat ) %>%
plot_freq( "Quality of the freezer",
fill_by=quality_storage_meat, wrap_by=NULL, my_labels=NULL ) +
scale_fill_manual("legend", values = c(brewer.pal(3, "Blues"),"brown","dark grey","light grey"))+
guides( "", fill = guide_legend(title="", nrow = 3, byrow = T))
## Scale for fill is already present.
## Adding another scale for fill, which will replace the existing scale.
#ggsave("figures/process_quality_freezer.png", width = 35, height = 25, units="cm", dpi=300 )
wash_labels= c(
"wash_veg_ready" = "Ready to eat",
"wash_veg_berries" = "Berries",
"wash_veg_pome_fr" = "Pomme Fruit",
"wash_veg_brassic" = "Brassica",
"wash_veg_cucurbi" = "Cucurbi",
"wash_veg_tomato" = "Tomato",
"wash_veg_peppers" = "Peppers",
"wash_veg_fungi" = "Fungi",
"wash_veg_herbs" = "Herbs",
"wash_veg_leafy_veg" = "Leafy vegetables",
"wash_veg_sprouts" = "Sprouts",
"wash_veg_leafy_misc" = "Misc",
"wash_veg_roots" = "Roots",
"wash_veg_stems" = "Stems" )
df_survey_process %>%
dplyr::select( starts_with("wash_veg"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "wash_"), names_to = "prep", values_to = "washed" ) %>%
calc_perc( prep, washed ) %>%
plot_freq( "Frequency of washing vegetables",
wrap_by=prep, fill_by=washed, my_labels=wash_labels )
#ggsave("figures/freq_wash_veg.png", width = 35, height = 25, units="cm" )
prep_labels = c( "prep_meat_cuts" = "Meat Cuts",
"prep_meat_mince" = "Minced Meat",
"prep_meat_sausage" = "Sausage" )
df_survey_process %>%
dplyr::select( starts_with("prep_meat"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
pivot_longer( cols=starts_with( "prep_"), names_to = "prep", values_to = "cooked" ) %>%
calc_perc( prep, cooked ) %>%
plot_freq( "Frequency of preparation style", cooked,
wrap_by=prep, my_labels=prep_labels )
#ggsave("figures/prep_style_meat.png", width = 25, height = 15, units="cm", dpi=300 )
labels_freq_raw_veg <- c(
"freq_fruit_raw_unpeeled" = "Raw unpeeled fruit",
"freq_veg_raw_smoothie" = "Raw vegetable smoothie",
"freq_veg_raw" = "Raw vegetables" )
df_survey_process %>%
dplyr::select( freq_fruit_raw_unpeeled, freq_veg_raw_smoothie, freq_veg_raw, id ) %>%
pivot_longer( -id, names_to="fruit", values_to="freq" ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc(fruit, freq ) %>%
plot_freq("Frequency of eating fruit or vegetables raw", freq,
wrap_by=fruit, my_labels=labels_freq_raw_veg )
#ggsave("figures/freq_raw_fruit_veg.png", width = 35, height = 25, units="cm", dpi=300 )
df_survey_type %>%
dplyr::select( type_fermented_sausage , id ) %>%
left_join( df_survey_info, by="id" ) %>%
filter(!is.na(type_fermented_sausage)) %>%
calc_perc( type_fermented_sausage ) %>%
plot_freq( "Type of fermented sausage preferred", type_fermented_sausage )
#ggsave("figures/type_fermented_sausage.png", width=40, height=20, units="cm" )
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_meatball, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_meatball ) %>%
plot_freq( "Volume of meatball consumed", vol_meat_meatball )
#ggsave("figures/vol_meatball.png", width=30, height = 15, units="cm")
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_sausage, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_sausage ) %>%
plot_freq( "Volume of sausage consumed", vol_meat_sausage )
#ggsave("figures/vol_sausage.png", width=30, height = 15, units="cm")
TODO: insert figures
df_survey_process %>%
dplyr::select( vol_meat_cut, id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_cut ) %>%
plot_freq( "Volume of meat cuts consumed", vol_meat_cut )
ggsave("figures/vol_meatcut.png", width=30, height = 15, units="cm")
Note, something was clearly wrong here. Respondents were asked to supply the number of 55 gram portions, but probably supplied the total weight. We have filtered the figure to only show 550g total or less. TODO: insert figures
df_survey_process %>%
left_join( df_survey_info, by="id" ) %>%
filter( vol_veg_raw < 10 ) %>%
mutate( vol_veg_raw = vol_veg_raw * 55 ) %>%
ggplot( ) +
geom_histogram( aes(vol_veg_raw, y=..density.., fill=country),
position="dodge", binwidth=20) +
geom_density( aes(vol_veg_raw, color=country), bw=30 ) +
scale_x_continuous( "Weight [g]" ) +
ggtitle( "Portion size raw vegetables" ) +
my_theme
## Warning: The dot-dot notation (`..density..`) was deprecated in ggplot2 3.4.0.
## ℹ Please use `after_stat(density)` instead.
#ggsave("figures/vol_raw_veg.png", width=25, height = 15, units="cm")
When preparing e.g. steak tartare some people may sample the meat for taste.
# TODO: check NA meaning
df_survey_process %>%
dplyr::select( starts_with("vol_meat_raw"), id ) %>%
left_join( df_survey_info, by="id" ) %>%
calc_perc( vol_meat_raw_sample ) %>%
plot_freq( "Amount of raw meat sampled", vol_meat_raw_sample )
ggsave("figures/raw_meat_sampled.png", width = 20, height = 15, units="cm")